Tabu Search Research Articles

Collaborative truck–drone–motorcycle delivery of emergency supplies for mountain wildfire suppression

This paper studies the transportation of emergency supplies for mountain wildfire suppression by trucks, drones, and motorcycles. The primary objective is to minimize the time of emergency supplies delivery. By examining the structure of mountainous road networks, the involvement of motorcycles and unmanned aerial vehicles (drones) in collaborative delivery operations, the risk associated with fire spread, and the optimal location for supplies distribution, this problem has been formulated as a collaborative truck–drone–motorcycle location-routing robust optimization model. The aim of introducing a robust optimization model is to ensure the feasibility of the routes under uncertain demand conditions. A box uncertainty set is employed to describe the uncertainty of emergent demands. An improved adaptive large neighborhood search (IALNS) algorithm is developed to solve this problem. A delete operator, a collaborative truck–drone delivery strategy, and a location strategy are introduced to handle various constraints involved in the problem, search the optimal location of the supplies distribution point, and compute vehicle routings. Our method has been compared with several existing algorithms, such as genetic algorithm, simulated annealing algorithm, tabu search algorithm, and whale optimization algorithm. Their average gap with IALNS are respectively 50.64%, 21.20%, 32.59%, and 19.13%. The experimental results demonstrate the efficacy and efficiency of the proposed model and algorithm.

Integrated crew organization and work zone scheduling for network-wide daily road pavement rehabilitation

This study develops a new integer-programming model to address the network-wide daily road pavement rehabilitation scheduling problem. In the model, the crew organization and work zone schedule are jointly optimized daily, with the objective of minimizing both the operational cost and user travel time. A day-to-day traffic dynamics model is applied to capture the non-equilibrium traffic evolution against network supply variation over the planning horizon, which leads to a simulation-based optimization problem. To solve this challenging problem, a two-stage hybrid heuristic solution method is proposed. In the first stage, hybrid tabu search (TS) meta-heuristics are comparatively developed to identify a group of active crew work routes without time slacks. The obtained crew routes are then fed to the second stage for work zone scheduling via a discrete compass search algorithm. Some important findings are obtained from numerical experiments. First, crew routing (or crew organization) is the dominant decision in the studied problem, and a desirable work zone schedule encourages a crew to execute the assigned tasks continually. The findings can be used to develop simplified and efficient solution algorithms. Second, the hybrid TS meta-heuristics developed for crew routing exhibit superior performance compared to other solution methods. Finally, a well-defined model for the current problem should consider both user travel time and operation cost. Our model enables decision-makers to make an effective trade-off between these two objectives. An effective measure is suggested to evaluate the cost-effectiveness of budget investment decisions when budgets are limited.

Penerapan Algoritma Tabu Search pada Capacitated Vehicle Routing Problem Pengangkutan Sampah di Kota Padang Sidempuan

Waste transportation activities are an example of a form of Capacitated Vehicle Routing Problem (CVRP) related to finding the minimum route. The Tabu Search algorithm is one of the metaheuristic methods that can guide the heuristic local search procedure to explore the solution area outside the local optimal point. The Tabu Search algorithm can be used to find the optimal VRP solution, namely the route that has the minimum total mileage by considering vehicle capacity. The purpose of this research is to determine the optimal route for garbage transportation in the Capacitated Vehicle Routing Problem (CVRP) model in Padang Sidempuan City using the Tabu Search algorithm. Based on the results of the study, it is concluded that the optimal route for transporting waste in the Capacitated Vehicle Routing Problem (CVRP) model in Padang Sidempuan City using the Tabu Search algorithm obtained the shortest route in iteration 1 with the route (12-11-10-9-8-7-6-5-4-3-2-1-0) and route length 16.55 km.

Integrated food delivery problem considering both single-order and multiple order deliveries

The global food delivery market is rapidly growing, and leading companies in Korea have recently introduced a single-order delivery service, wherein a courier picks up customers’ food and delivers it immediately without visiting other places. Although this delivery service is associated with a high delivery fee, it is popular among customers who prefer fresh and fast delivery, unlike multiple-order delivery services, which handle orders from several customers simultaneously. Nevertheless, the coexistence of both delivery services has led couriers to prefer single-order deliveries because they are more profitable, resulting in intensified competition among couriers. Furthermore, there are cases where the courier visits other restaurants or customers without immediately delivering food to customers who use the single-order delivery service, making it a multiple-order delivery service, which leads to customer dissatisfaction. To address these issues, this study proposes a mixed-integer linear programming model for optimizing order assignment and routing in an environment where both single-order and multiple-order deliveries coexist. The tabu search was used to solve medium-sized and large-sized problems, and several food delivery models were configured by referencing the operating methods of the platforms. Through experiments, we demonstrate that the proposed model is more efficient in properly delivering food to customers for both single and multiple orders than other food delivery models. We also analyzed the impact of various variables through a sensitivity analysis, providing insights into the platform.

Retraction Note: Genetic algorithm-based tabu search for optimal energy-aware allocation of data center resources

Retraction Note: Genetic algorithm-based tabu search for optimal energy-aware allocation of data center resources

A robust optimisation approach for the placement of forest fire suppression resources

AbstractThis research develops an initial attack plan for combating forest fires in any wildland areas susceptible to fire outbreaks. To be eligible for such a plan, the landscape must have been previously mapped and modelled concerning spatial and topographic data and fuel levels. Thus, when ignition occurs, one can predict the expected fire behaviour in terms of spread direction and rate of spread. With such information, decisions can be taken on where and when to position the suppression resources. This paper extends a recent contribution to this subject, generalising each node's resource requirement, allowing a more precise modelling of non‐homogeneous landscapes. Moreover, we treat the cases where the estimated number of resources may not be sufficient to deal with the fire intensity, which becomes revealed only at the fire scene. In such cases, additional resources may be needed to contain the fire effectively. This worst‐case approach is modelled with the support of the robust optimisation paradigm. We propose a deterministic mathematical programming model, a robust optimisation counterpart, and a robust tabu search (RoTS) algorithm. We adapt instances from the literature, which are optimally solved by a commercial solver and used for assessing the quality of the RoTS. The proposed algorithm could optimally solve 94 of 96 instances. Finally, we conducted a Monte Carlo simulation as part of a risk analysis assessment of the generated solutions.

Aviation armament system-of-systems modeling and identification method of vulnerable nodes based on interdependent network

Aiming at the problem that it is difficult to build model and identify the vulnerable equipment for aviation armament System-of-Systems (SoS) due to complex equipment interaction relationships and high confrontation, the interdependent network theory is introduced to solve it. Firstly, a two-layer heterogeneous interdependent network model for aviation armament SoS is proposed, which reflects the information interaction, functional dependency and inter-network dependence effectively. Secondly, using the attack cost to describe the confrontation process and taking the comprehensive impact on kill chains as the entry point, the node importance index and the attack cost measurement method are constructed. Thirdly, the identification of vulnerable nodes is transformed into the optimization problem of node combinatorial selection, and the vulnerable node identification method based on tabu search is proposed. Based on vulnerable nodes, a robustness enhancement strategy for aviation armament SoS network is presented. Finally, the above methods are used to an aerial confrontation SoS, and the results verify the rationality and effectiveness of the proposed methods.

Sequential testing in batches with resource constraints

This paper studies a problem of determining the state of a system through costly tests of its components, where components can be tested simultaneously in batches to exploit economies of scale. This problem is a generalization of the classical sequential testing problem and it has applications in various settings, including machine maintenance, disease diagnosis, and new product development. We prove that the problem is strongly NP-hard, model it as a mixed-integer programming formulation, and we also propose a dynamic program for it. Additionally, we design a tabu search and a hybrid solution method that combines a tabu search metaheuristic and a proximity search matheuristic. Based on extensive computational experiments, we find that the dynamic program can solve instances with up to 25 components within a 15-minute time limit and 16 GB of RAM. With respect to larger instances, the proposed metaheuristic and hybrid method are better than a greedy heuristic.

An Effective Local Search Algorithm for Flexible Job Shop Scheduling in Intelligent Manufacturing Systems

As one of the most classical scheduling problems, flexible job shop scheduling problems (FJSP) find widespread applications in modern intelligent manufacturing systems. However, the majority of meta-heuristic methods for solving FJSP in the literature are population-based evolutionary algorithms, which are complex and time-consuming. In this paper, we propose a fast effective single-solution based local search algorithm with an innovative adaptive weighting technique (AWLS) for solving FJSP. The adaptive weighting technique assigns weights to each operation and adaptively updates them during the exploration. AWLS integrates a Tabu Search strategy and the adaptive weighting technique to smooth the landscape of the search space and enhance the exploration diversity. Computational experiments on 313 well-known benchmark instances demonstrate that AWLS is highly competitive with state-of-the-art algorithms in terms of both solution quality and computational efficiency, despite of its simplicity. Specifically, AWLS improves the previous best-known results in the literature on 33 instances and match the best-known results on the remaining ones except for only one under the same time limit of up to 300 s. As a strongly Non-deterministic Polynomia (NP)-hard problem which has been extensively studied for nearly half a century, breaking the records on these classic instances is an arduous task. Nevertheless, AWLS establishes new records on 8 challenging instances whose previous best records were established by a state-of-the-art meta-heuristic algorithm and a famous industrial solver.

A hybrid genetic tabu search algorithm for distributed job-shop scheduling problems

The distributed job-shop scheduling problem (DJSP) is an extension of the traditional job-shop scheduling problem, which are composed of two sub-problems, assigning jobs to suitable factories and deciding the operation sequence on machines. To evaluate the performance of algorithms for solving DJSP, several famous benchmark instances have been proposed, and most of these instances have not been solved so far. This paper proposes a hybrid genetic tabu search algorithm (HGTSA) for solving DJSP. The proposed HGTSA combines the global search ability of the genetic algorithm (GA) and the local search ability of the tabu search (TS) well. In GA part, a crossover operation and a mutation operation are devised based on the critical factory. The two operations can effectively improve the discreteness of the population. In TS part, a tabu search procedure is performed on the critical factory. The procedure can effectively enhance the local search ability of HGTSA. For evaluating the performance of HGTSA, it has been compared with five classical algorithms on 240 benchmark instances. The computational results show the efficiency and effectiveness of HGTSA for solving DJSP. In particular, the proposed HGTSA updates the upper bounds for 235 out of these difficult instances.

Designing a Draft for a Metaheuristic Curriculum Evaluation Model (MCEM) Based on the Examination of Various Metaheuristic Artificial Intelligence Optimization Applications

This paper explores the integration of metaheuristic artificial intelligence (AI) optimization algorithms into the process of curriculum evaluation, proposing a novel approach that could enhance educational outcomes. A meta-synthesis of the existing literature on the application of AI optimization techniques—such as tabu search, simulated annealing, genetic algorithms, and ant colony optimization—in educational contexts was conducted. This study revealed a scarcity of direct applications of these algorithms in curriculum evaluation, thus identifying a gap in research and an opportunity for exploration. We proposed detailed models for adapting various metaheuristic optimization algorithms to assess curriculum components, including objectives, content, teaching methodologies, and assessment strategies. Our paper synthesizes insights from the literature review and suggests avenues for experimental studies to assess the effectiveness of AI optimization algorithms across diverse educational levels and curricula. Furthermore, we introduce a draft of the Metaheuristic Curriculum Evaluation Model (MCEM), synthesized from the reviewed optimization models and curriculum evaluation processes. This exploration into the integration of metaheuristic AI optimization algorithms within curriculum evaluation highlights a promising frontier in educational research. By detailing potential applications, addressing methodological rigor, and considering context-specific nuances, this paper lays the groundwork for future studies that could evaluate how curricula are developed, evaluated, and optimized from a different perspective.

Two‐stage algorithm for traffic signal optimization and web‐service system development

AbstractEfficient control of traffic signals for vehicles and pedestrians at intersections is critical for relieving traffic congestion. Considering the unique characteristics of intersections, such as the number of roads, the presence or absence of crosswalks, road geometric shapes, and traffic demand patterns, an appropriate phase sequence and duration for traffic signals must be established at each intersection. This paper proposes a simulation‐based two‐stage algorithm comprising integer‐constrained Adam (ICA) and tabu search (TS) to optimize the phase sequence and duration for arbitrary intersections with arbitrary traffic‐demand patterns. The ICA promptly identifies a promising region in which a global optimal solution is likely to be obtained, whereas TS determines the best solution near the region. The performance of the proposed algorithm that optimizes phase durations with fixed phase sequence is evaluated against several baseline methods using 24 instances across six actual intersections. Experimental results show that the proposed algorithm reduces the average travel time by 20.2% compared with existing traffic signals within a computation time of 4 min, thus providing a near‐optimal solution eight times faster than commonly used population‐based metaheuristics. Furthermore, the algorithm demonstrates robust performance across heterogeneous vehicles and recommends the best phase sequence that effectively alleviates congestion in current traffic signal systems. The optimized phase sequence with best phase durations further reduces the average travel time by approximately 11.3% compared with the existing phase sequence with best phase durations at an actual intersection. To facilitate its widespread use, a free, open web‐service system named “Smart Intersection for Traffic Efficiency” is developed, which enables users to optimize traffic signal systems without requiring optimization background or simulation knowledge.

Influence of encoding and neighborhood in landscape analysis and tabu search performance for job shop scheduling problem

Fitness landscape analysis is used to understand search spaces of combinatorial problems that can hardly be solved exactly, such as the job shop scheduling problem. We analyze the influence of the solution encodings and the neighborhood operators on usual metrics for landscape analysis, and try to correlate the results to the performance of a local search method such as tabu search using these encodings and operators for a wide range of instances of the job shop scheduling problem.

Dual-Neighborhood Tabu Search for Computing Stable Extensions in Abstract Argumentation Frameworks

Abstract argumentation has become one of the important fields of artificial intelligence. This paper proposes a dual-neighborhood tabu search (DNTS) method specifically designed to find a single stable extension in abstract argumentation frameworks. The proposed algorithm implements an improved dual-neighborhood strategy incorporating a fast neighborhood evaluation method. In addition, by introducing techniques such as tabu and perturbation, this algorithm is able to jump out of the local optimum, which significantly improves the performance of the algorithm. In order to evaluate the effectiveness of the method, the performance of the algorithm on more than 300 randomly generated benchmark datasets was studied and compared with the algorithm in the literature. In the experiment, DNTS outperforms the other method regarding time consumption in more than 50 instances and surpasses the other meta-heuristic method in the number of solved cases. Further analysis shows that the initialization method, the tabu strategy, and the perturbation technique help guarantee the efficiency of the proposed DNTS.

A hybrid method for the intelligent effectiveness management of production operators

AbstractThe paper presents a hybrid method for the effective management of human resources when executing production processes. The method was proposed for managing production operators in systems of relatively high complexity—with the possibility of multiple alternative ways of designating operators to production stations. The method combines a novel approach of assessing the effectiveness of production operators, the use of an intelligent algorithm and the risk assessment method. The concept of effectiveness was used in the sense of a praxeological approach. Availability, performance and quality were defined as its components, and were evaluated through the prism of losses occurring in production processes. The aim of the study is to develop a hybrid method in order to delegate production operators to tasks, not only on the basis of a competence metric, but also on the basis of an individual effectiveness indicator and the risk assessment of production process stages. For this purpose, a novel method of assessing the effectiveness of the use of available resources when implementing production processes (based on a set of quantitative indicators and taking into account the categorization of production losses) was juxtaposed using the Tabu Search algorithm and the FMEA method. The method enables a near-optimal solution to be generated—one in which operators are assigned to production tasks in the most effective way possible, which in turn minimizes both the risk of production delays and quality defects due to operator fault.

Incorporating machine learning and optimization techniques for assigning patients to operating rooms by considering fairness policies

Operating rooms (ORs) are key to a hospital's profitability, and increasing their benefits helps decrease surgical costs, minimize patient waiting times, and boost the number of patients admitted for surgery. This study presents a mixed-integer linear programming (MILP) model to address the issue of treating inpatients in operating rooms waiting for admission to the referral department. Moreover, it proposes a practical algorithm that minimizes overtime costs in operating rooms and increases patient satisfaction. Our model uses an integrated machine learning - tabu search (ML-TS) method. The proposed model uses differential ML and heuristic rules to achieve enhanced performance and scalability in solving complex optimization problems. A fairness policy is implemented, combined with an optimization method, to guide patient prioritization. This approach considers factors like urgency, waiting times, or specific patient needs and helps determine the order in which patients are scheduled for surgery. The outcomes imply that patient adaptability has led to savings ranging from 50% to 65% per surgery. Additionally, the Machine Learning-Tabu Search (ML-TS) algorithm has increased performance by about 35%. These algorithms also let the OR manager define the size of the uncertainty set and regulate overtime costs while accommodating surgeons' preferences as managerial implications insight. It adeptly navigates the solution space, identifies feasible solutions, and accelerates convergence.

A new hybrid genetic algorithm with tabu search for solving the temporal coverage problem using rotating directional sensors

AbstractOne of the most important problems in directional sensor networks is coverage problem. The coverage can be measured in two ways: positional or temporal. In temporal coverage, the directional sensors rotate periodically round themselves in a repetitive process. Thus, in each time slot, those targets that are positioned within the sensor nodes radius receive their desired coverage. In this model, if a target is left uncovered, it is said that the target has remained in darkness. The main task defined for the temporal coverage model is the minimization of the total dark time for all the targets in the network. This problem has been solved by greedy‐based algorithms in last studies. Greedy‐based algorithms are able to solve the temporal coverage problem in real time. Remember that the performance of greedy algorithms is extremely dependent on the closeness of optimal solution and initial candidates. For this reason, greedy algorithms may obtain local minima due to heuristic search. As far as we know meta‐heuristic algorithms have not been used in past researches to solve such problems. For solving this problem, in this paper two algorithms were developed, GA‐based and hybridized model comprising genetic algorithms and tabu search. A new model was suggested for the chromosome in genetic algorithm. To evaluate the performance of the developed algorithms, they were compared with randomized scenario and greedy‐based algorithm presented in last studies. For better comparison, several parameters, including total dark time, number of sensors, number of targets, sector angle, sensing range were taken into account. The results obtained from the comparison of the algorithms indicated that the developed algorithms are effective in solving the temporal coverage problem in terms of minimizing the total dark time of the targets.

Municipal street‐sweeping area generation with route optimization

AbstractStreet sweeping is used in urban municipalities worldwide but requires a great deal of planning for large‐scale implementation. Municipalities typically make use of the macro‐ and microapproach by using operational areas in which routes are assigned, but creating operational areas without an understanding of the expected workload, number of routes, and the travel distance to and from the depot may lead to increased statistics. In this paper, a combination of heuristic approaches is used to assign street‐sweeping areas on the macroscale and generate street‐sweeping routes on the microscale. For the area assignment, a two‐stage cluster approach is proposed, making use of the weighted k‐means algorithm and differential evolution. For the route optimization, a three‐phase augment merge algorithm is used to create initial solutions, the u‐turns are minimized with a modified version of Hierholzer's algorithm and tabu search, and the remaining u‐turns are removed using a forward‐searching ant colony optimization. A case study in The City of Oshawa, Canada, was used to verify the proposed methodology, and all metrics were theoretically improved.

Efficiency increment of CFD modeling by using ANFIS artificial intelligence for thermal-based separation modeling

This study presents a comprehensive analysis of predicting the temperature in a vacuum membrane distillation (VMD) process. The simulation was carried out via computational fluid dynamics (CFD) as well as machine learning. CFD was performed for obtaining temperature distribution in the feed solution, and the calculated temperature was used for training several machine learning models. The dataset comprises over 13,000 observations, which provide a rich source for modeling complex relationships. Three sophisticated regression models were employed: Adaptive Neuro-Fuzzy Inference System (ANFIS), Kernel Ridge Regression (KRR), and Multi-Layer Perceptron (MLP). ANFIS was chosen for its hybrid nature, combining neural networks and fuzzy logic, effectively capturing intricate non-linear relationships in data. ANFIS performance in fitting the data was compared with the other models. Hyper-parameter optimization for these models was conducted using the Tabu Search algorithm to ensure optimal performance. The ANFIS model demonstrated superior performance with an R2 score of 0.9964513 on the training set and 0.9964507 on the test set, alongside a MSE of 0.037655 and a MAE of 0.168272. The robustness of ANFIS was further confirmed by a 3-fold cross-validation mean R2 score of 0.9964579 and a standard deviation of 3.3619616e−05.

A Hybrid Particle Swarm Optimization with Tabu Search for Optimizing Aid Distribution Route

A Hybrid Particle Swarm Optimization with Tabu Search for Optimizing Aid Distribution Route